EVJVQA Challenge: Multilingual Visual Question Answering

• URL: http://arxiv.org/abs/2302.11752v5
• Date: Wed, 17 Apr 2024 04:09:25 GMT
• Title: EVJVQA Challenge: Multilingual Visual Question Answering
• Authors: Ngan Luu-Thuy Nguyen, Nghia Hieu Nguyen, Duong T. D Vo, Khanh Quoc Tran, Kiet Van Nguyen,
• Abstract summary: Visual Question Answering (VQA) is a challenging task of natural language processing (NLP) and computer vision (CV) EVJVQA is used as a benchmark dataset for the challenge of multilingual visual question answering at the 9th Workshop on Vietnamese Language and Speech Processing (VLSP 2022) We present details of the organization of the challenge, an overview of the methods employed by shared-task participants, and the results.
• Score: 1.4641199499831683
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Visual Question Answering (VQA) is a challenging task of natural language processing (NLP) and computer vision (CV), attracting significant attention from researchers. English is a resource-rich language that has witnessed various developments in datasets and models for visual question answering. Visual question answering in other languages also would be developed for resources and models. In addition, there is no multilingual dataset targeting the visual content of a particular country with its own objects and cultural characteristics. To address the weakness, we provide the research community with a benchmark dataset named EVJVQA, including 33,000+ pairs of question-answer over three languages: Vietnamese, English, and Japanese, on approximately 5,000 images taken from Vietnam for evaluating multilingual VQA systems or models. EVJVQA is used as a benchmark dataset for the challenge of multilingual visual question answering at the 9th Workshop on Vietnamese Language and Speech Processing (VLSP 2022). This task attracted 62 participant teams from various universities and organizations. In this article, we present details of the organization of the challenge, an overview of the methods employed by shared-task participants, and the results. The highest performances are 0.4392 in F1-score and 0.4009 in BLUE on the private test set. The multilingual QA systems proposed by the top 2 teams use ViT for the pre-trained vision model and mT5 for the pre-trained language model, a powerful pre-trained language model based on the transformer architecture. EVJVQA is a challenging dataset that motivates NLP and CV researchers to further explore the multilingual models or systems for visual question answering systems. We released the challenge on the Codalab evaluation system for further research.

Related papers

• WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines [74.25764182510295]
  Vision Language Models (VLMs) often struggle with culture-specific knowledge, particularly in languages other than English. We introduce World Cuisines, a massive-scale benchmark for multilingual and multicultural, visually grounded language understanding. This benchmark includes a visual question answering (VQA) dataset with text-image pairs across 30 languages and dialects, spanning 9 language families and featuring over 1 million data points.
  arXiv  Detail & Related papers  (2024-10-16T16:11:49Z)
• CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark [68.21939124278065]
  Culturally-diverse multilingual Visual Question Answering benchmark designed to cover a rich set of languages and cultures. CVQA includes culturally-driven images and questions from across 30 countries on four continents, covering 31 languages with 13 scripts, providing a total of 10k questions. We benchmark several Multimodal Large Language Models (MLLMs) on CVQA, and show that the dataset is challenging for the current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-06-10T01:59:00Z)
• MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering [58.92057773071854]
  We introduce MTVQA, the first benchmark featuring high-quality human expert annotations across 9 diverse languages. MTVQA is the first benchmark featuring high-quality human expert annotations across 9 diverse languages.
  arXiv  Detail & Related papers  (2024-05-20T12:35:01Z)
• Can a Multichoice Dataset be Repurposed for Extractive Question Answering? [52.28197971066953]
  We repurposed the Belebele dataset (Bandarkar et al., 2023), which was designed for multiple-choice question answering (MCQA) We present annotation guidelines and a parallel EQA dataset for English and Modern Standard Arabic (MSA). Our aim is to enable others to adapt our approach for the 120+ other language variants in Belebele, many of which are deemed under-resourced.
  arXiv  Detail & Related papers  (2024-04-26T11:46:05Z)
• ViCLEVR: A Visual Reasoning Dataset and Hybrid Multimodal Fusion Model for Visual Question Answering in Vietnamese [1.6340299456362617]
  We introduce the ViCLEVR dataset, a pioneering collection for evaluating various visual reasoning capabilities in Vietnamese. We conduct a comprehensive analysis of contemporary visual reasoning systems, offering valuable insights into their strengths and limitations. We present PhoVIT, a comprehensive multimodal fusion that identifies objects in images based on questions.
  arXiv  Detail & Related papers  (2023-10-27T10:44:50Z)
• HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language [1.3476084087665703]
  HaVQA is the first multimodal dataset for visual question-answering tasks in the Hausa language. The dataset was created by manually translating 6,022 English question-answer pairs, which are associated with 1,555 unique images from the Visual Genome dataset.
  arXiv  Detail & Related papers  (2023-05-28T10:55:31Z)
• OpenViVQA: Task, Dataset, and Multimodal Fusion Models for Visual Question Answering in Vietnamese [2.7528170226206443]
  We introduce the OpenViVQA dataset, the first large-scale dataset for visual question answering in Vietnamese. The dataset consists of 11,000+ images associated with 37,000+ question-answer pairs (QAs) Our proposed methods achieve results that are competitive with SOTA models such as SAAA, MCAN, LORA, and M4C.
  arXiv  Detail & Related papers  (2023-05-07T03:59:31Z)
• From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Data [27.160303686163164]
  Visual question answering (VQA) for remote sensing scene has great potential in intelligent human-computer interaction system. No object annotations are available in RSVQA datasets, which makes it difficult for models to exploit informative region representation. There are questions with clearly different difficulty levels for each image in the RSVQA task. A multi-level visual feature learning method is proposed to jointly extract language-guided holistic and regional image features.
  arXiv  Detail & Related papers  (2022-05-06T11:37:00Z)
• Delving Deeper into Cross-lingual Visual Question Answering [115.16614806717341]
  We show that simple modifications to the standard training setup can substantially reduce the transfer gap to monolingual English performance. We analyze cross-lingual VQA across different question types of varying complexity for different multilingual multimodal Transformers.
  arXiv  Detail & Related papers  (2022-02-15T18:22:18Z)
• Multilingual Answer Sentence Reranking via Automatically Translated Data [97.98885151955467]
  We present a study on the design of multilingual Answer Sentence Selection (AS2) models, which are a core component of modern Question Answering (QA) systems. The main idea is to transfer data, created from one resource rich language, e.g., English, to other languages, less rich in terms of resources.
  arXiv  Detail & Related papers  (2021-02-20T03:52:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.